Motion Planning Approach Considering Localization Uncertainty

Artuñedo, Antonio; Villagrá, Jorge; Godoy, Jorge; Castillo, M. Dolores del

doi:10.1109/tvt.2020.2985546

Cited by 29 publications

(10 citation statements)

References 37 publications

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“…This method reduces the information loss due to data discretization into cells. Likewise, uncertainty related to object’s location is taken into account following the method proposed in [ 43 ]. This is done once per object.…”

Section: Enhanced Perceptionmentioning

confidence: 99%

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Godoy

Jiménez

Artuñedo

et al. 2021

Sensors

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Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

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Section: Enhanced Perceptionmentioning

confidence: 99%

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Godoy

Jiménez

Artuñedo

et al. 2021

Sensors

Self Cite

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“…Planning Under Pose Uncertainty: The task of planning robust trajectories under pose uncertainty has been studied in the past, with previous methods formulating it as a continuous POMDP which can be solved with an iterative linearquadratic-Gaussian method [45], or as an optimal control problem solved using model-predictive control [17]. More recently, Artuñedo et al [1] focus on autonomous vehicles and incorporate the pose uncertainty in a probabilistic map representation that is then leveraged by a sampling-based planner. However, none of these approaches model other dynamic actors and the uncertainty in their respective motion, and do not study the complex interplay between pose uncertainty and state-of-the-art perception systems.…”

Section: Localizationmentioning

confidence: 99%

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Phillips¹,

Martínez²,

Bârsan³

et al. 2021

Preprint

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Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning. However, these systems often assume that the car is accurately localized against a high-definition map. In this paper we question this assumption, and investigate the issues that arise in state-of-the-art autonomy stacks under localization error. Based on our observations, we design a system that jointly performs perception, motion forecasting, and localization. Our architecture is able to reuse computation between both tasks, and is thus able to correct localization errors efficiently. We show experiments on a large-scale autonomy dataset, demonstrating the efficiency and accuracy of our proposed approach.

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“…Many different variations based on this algorithm have been proposed for path planning: Hybrid A* [26], Theta* [27], Filed D* [28], D* Lite [29], etc. Besides, several planning approaches [30,31] have been proposed to increase the system reliability, considering the localization uncertainty problem.…”

Section: Path and Trajectory Planning Algorithmmentioning

confidence: 99%

Safe Vehicle Trajectory Planning in an Autonomous Decision Support Framework for Emergency Situations

Sainct

Gruyer

et al. 2021

Applied Sciences

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For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal trajectory planning. The last stage addresses the vehicle control/command. This paper proposes an efficient solution to the second stage and improves a virtual Cooperative Pilot (Co-Pilot) already proposed in 2012. This paper thus introduces a trajectory planning algorithm for automated vehicles (AV), specifically designed for emergency situations and based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy. This algorithm is an extended version of Elastic Band (EB) with no fixed final position. A set of trajectory nodes is iteratively deduced from obstacles and constraints, thus providing flexibility, fast computation, and physical realism. After introducing the project framework for risk management and the general concept of ADSF, the emergency algorithm is presented and tested under Matlab software. Finally, the Decision-Support framework is implemented under RTMaps software and demonstrated within Pro-SiVIC, a realistic 3D simulation environment. Both the previous virtual Co-Pilot and the new emergency algorithm are combined and used in a near-accident situation and shown in different risky scenarios.

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Motion Planning Approach Considering Localization Uncertainty

Cited by 29 publications

References 37 publications

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Safe Vehicle Trajectory Planning in an Autonomous Decision Support Framework for Emergency Situations

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